Learning Explicit Single-Cell Dynamics Using ODE Representations
Jan-Philipp von Bassewitz, Adeel Pervez, Marco Fumero, Matthew Robinson, Theofanis Karaletsos, Francesco Locatello
细胞分化的动力学模型是推进对与这一过程相关的疾病的理解和治疗(如癌症)的基础。 随着单细胞数据集的快速增长,这也成为机器学习特别有前途的活跃领域。 然而,目前最先进的模型依赖于计算昂贵的最佳传输预处理和多阶段训练,同时也没有发现明确的基因相互作用。 为了应对这些挑战,我们提出了Cell-Mechanistic Neural Networks(Cell-MNN),这是一种编码器解码器架构,其潜在表示是一种局部线性化的ODE,可以控制细胞从茎到组织细胞的动力学。 Cell-MNN是完全端到端的(除了标准的PCA预处理),其ODE表示明确学习生物学上一致和可解释的基因相互作用。 经验上,我们表明Cell-MNN在单细胞基准测试中实现了竞争表现,在扩展到更大的数据集和跨多个数据集的联合训练方面超过了最先进的基线,同时还学习了可解释的基因相互作用,我们根据TRRUST基因相互作用数据库进行了验证。
Modeling the dynamics of cellular differentiation is fundamental to advancing the understanding and treatment of diseases associated with this process, such as cancer. With the rapid growth of single-cell datasets, this has also become a particularly promising and active domain for machine learning. Current state-of-the-art models, however, rely on computationally expensive optimal transport preprocessing and multi-stage training, while also not discovering explicit gene interactions. To address...